Bayesian counterfactual analysis of the sources of the great moderation

Kim Chang-Jin, James Morley, Jeremy Piger

    Research output: Contribution to journalArticlepeer-review

    14 Citations (Scopus)

    Abstract

    We use counterfactual experiments to investigate the sources of the large volatility reduction in US real GDP growth in the 1980s. Contrary to an existing literature that conducts counterfactual experiments based on classical estimation and point estimates, we consider Bayesian analysis that provides a straightforward measure of estimation uncertainty for the counterfactual quantity of interest. Using Blanchard and Quah's (1989) structural VAR model of output growth and the unemployment rate, we find strong statistical support for the idea that a counterfactual change in the size of structural shocks alone, with no corresponding change in the propagation of these shocks, would have produced the same overall volatility reduction as what actually occurred. Looking deeper, we find evidence that a counterfactual change in the size of aggregate supply shocks alone would have generated a larger volatility reduction than a counterfactual change in the size of aggregate demand shocks alone. We show that these results are consistent with a standard monetary VAR, for which counterfactual analysis also suggests the importance of shocks in generating the volatility reduction, but with the counterfactual change in monetary shocks alone generating a small reduction in volatility.

    Original languageEnglish
    Pages (from-to)173-191
    Number of pages19
    JournalJournal of Applied Econometrics
    Volume23
    Issue number2
    DOIs
    Publication statusPublished - 2008 Mar

    ASJC Scopus subject areas

    • Social Sciences (miscellaneous)
    • Economics and Econometrics

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